Abstract
In this paper, a method for characterizing cerebral infarction (CI) utilizing spontaneous electroencephalogram (EEG) is described. We obtained the time–frequency representations (TFRs) of EEG signals recorded from both normal subjects and CI patients. The corresponding characteristics were depicted by relative frequency band energy (RFBE) and Shannon entropy (SE) of TFR. Comparing with the normal subjects, the CI patients had some changes in EEG as follows: (1) delta and theta rhythms were attenuated while beta and gamma rhythms were enhanced, and the changes of delta and beta were more significant, (2) alpha was also blocked with eyes open, however the blocking action was less evident, (3) SE increase was pronounced. Consequently, the quantitative EEG methods are promising tools to provide helpful and sensitive information for the detection and diagnose of CI.
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Zhang, L., He, C., He, W. (2010). Characterization of Cerebral Infarction in Multiple Channel EEG Recordings Based on Quantifications of Time-Frequency Representation. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_11
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DOI: https://doi.org/10.1007/978-3-642-15615-1_11
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